class_docstr: BERT encoder for pre-tokenized text.
class_docstr_tok: ["BERT","encoder","for","pre-tokenized","text","."]
code: def output_spec(self):
      return {
          "top_layer_embs": lit_types.TokenEmbeddings(),
          "wpm_tokens": lit_types.Tokens(),
          "offsets": lit_types.SubwordOffsets(
              align_in="tokens", align_out="wpm_tokens"
          ),
      }
  
file_path: lit/lit_nlp/examples/coref/encoders.py
imports: import os
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.coref import retokenize
  from lit_nlp.examples.models import model_utils
  from lit_nlp.lib import utils
  import numpy
  import tensorflow
  import transformers
label_desc: Use image node_id 8 for calling the BertEncoderWithOffsets obj's underlying member method code with example usage: obj.output_spec() and returns: dict